Raising young children in multilingual Montreal: Chinese-Canadian parents' language choices and storybook reading style
Bibliographic record
Abstract
Despite growing research on parent-child storybook reading in bilingual contexts, little is known about the reading styles that bi- or multilingual parents, particularly immigrant parents, employ with their children, and how these practices vary depending on the language of the book. This laboratory-based observational study examined how Chinese-Canadian parents living in a multilingual environment interact with their young child to support language learning during storybook reading. Thirty parent-preschooler dyads living in Montreal, Quebec where French is the dominant language and English is also widely spoken, participated. Parents were observed sharing a storybook with their child with a Chinese book and a French or an English book, depending on parental choice. The results showed that when reading either a French or an English book, parents used significantly more dialogic talk than when reading the Chinese book. Specifically, parents asked more literal questions to assess whether their child understood the vocabulary and text or to teach vocabulary. In both conditions, parents asked few inferential and distancing questions that place higher cognitive demands on children but are important to story comprehension and foster their engagement with reading. Results also revealed that parents and children preferred to interact in Chinese even when reading a French or an English book. The findings could guide interventions with immigrant families to optimize parental support of language learning in bilingual and multilingual contexts and promote positive experiences for both parents and their child.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".